PREDICTING THE FUTURE MOVEMENT OF AGENTS IN AN ENVIRONMENT USING OCCUPANCY FLOW FIELDS

    公开(公告)号:US20220301182A1

    公开(公告)日:2022-09-22

    申请号:US17698930

    申请日:2022-03-18

    Applicant: Waymo LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting the future movement of agents in an environment. In particular, the future movement is predicted through occupancy flow fields that specify, for each future time point in a sequence of future time points and for each agent type in a set of one or more agent types: an occupancy prediction for the future time step that specifies, for each grid cell, an occupancy likelihood that any agent of the agent type will occupy the grid cell at the future time point, and a motion flow prediction that specifies, for each grid cell, a motion vector that represents predicted motion of agents of the agent type within the grid cell at the future time point.

    PERFORMING POINT CLOUD TASKS USING MULTI-SCALE FEATURES GENERATED THROUGH SELF-ATTENTION

    公开(公告)号:US20230351691A1

    公开(公告)日:2023-11-02

    申请号:US18120989

    申请日:2023-03-13

    Applicant: Waymo LLC

    CPC classification number: G06T17/20 G06T2210/56

    Abstract: Methods, systems, and apparatus for processing point clouds using neural networks to perform a machine learning task. In one aspect, a system comprises one or more computers configured to obtain a set of point clouds captured by one or more sensors. Each point cloud includes a respective plurality of three-dimensional points. The one or more computers assign the three-dimensional points to respective voxels in a voxel grid, where the grid of voxels includes non-empty voxels to which one or more points are assigned and empty voxels to which no points are assigned. For each non-empty voxel, the one or more computers generate initial features based on the points that are assigned to the non-empty voxel. The one or more computers generate multi-scale features of the voxel grid, and the one or more computers generate an output for a point cloud processing task using the multi-scale features of the voxel grid.

    EFFICIENT SEARCH FOR DATA AUGMENTATION POLICIES

    公开(公告)号:US20240232647A9

    公开(公告)日:2024-07-11

    申请号:US18492646

    申请日:2023-10-23

    Applicant: Waymo LLC

    CPC classification number: G06N3/0985

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model on training data. In one aspect, one of the methods include: obtaining a training data set comprising a plurality of training inputs; obtaining data defining an original search space of a plurality of candidate data augmentation policies; generating, from the original search space, a compact search space that has one or more global hyperparameters; and training the machine learning model on the training data using one or more final data augmentation policies generated from the compact search space.

    EFFICIENT SEARCH FOR DATA AUGMENTATION POLICIES

    公开(公告)号:US20240135195A1

    公开(公告)日:2024-04-25

    申请号:US18492646

    申请日:2023-10-22

    Applicant: Waymo LLC

    CPC classification number: G06N3/0985

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model on training data. In one aspect, one of the methods include: obtaining a training data set comprising a plurality of training inputs; obtaining data defining an original search space of a plurality of candidate data augmentation policies; generating, from the original search space, a compact search space that has one or more global hyperparameters; and training the machine learning model on the training data using one or more final data augmentation policies generated from the compact search space.

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